信頼できるGoogle Cloud Certified Professional-Machine-Learning-Engineer問題集PDF 2023年07月31日最近更新された問題
必ず合格できるGoogle Professional-Machine-Learning-Engineer試験正確な150問題と解答あります
Google Professional Machine Learning Engineer認定資格を取得することは、Google Cloudの技術を使用して機械学習モデルを設計および実装する能力を証明し、キャリアアップの機会と就職先の増加につながることがあります。これは、機械学習の分野で高く評価され、世界中の業界専門家に認められた資格です。
質問 # 46
One of your models is trained using data provided by a third-party data broker. The data broker does not reliably notify you of formatting changes in the dat a. You want to make your model training pipeline more robust to issues like this. What should you do?
- A. Use custom TensorFlow functions at the start of your model training to detect and flag known formatting errors.
- B. Use tf.math to analyze the data, compute summary statistics, and flag statistical anomalies.
- C. Use TensorFlow Transform to create a preprocessing component that will normalize data to the expected distribution, and replace values that don't match the schema with 0.
- D. Use TensorFlow Data Validation to detect and flag schema anomalies.
正解：C
質問 # 47
A Data Scientist received a set of insurance records, each consisting of a record ID, the final outcome among
200 categories, and the date of the final outcome. Some partial information on claim contents is also provided, but only for a few of the 200 categories. For each outcome category, there are hundreds of records distributed over the past 3 years. The Data Scientist wants to predict how many claims to expect in each category from month to month, a few months in advance.
What type of machine learning model should be used?
- A. Classification month-to-month using supervised learning of the 200 categories based on claim contents.
- B. Classification with supervised learning of the categories for which partial information on claim contents is provided, and forecasting using claim IDs and timestamps for all other categories.
- C. Reinforcement learning using claim IDs and timestamps where the agent will identify how many claims in each category to expect from month to month.
- D. Forecasting using claim IDs and timestamps to identify how many claims in each category to expect from month to month.
正解：B
解説：
Explanation
質問 # 48
You are training a TensorFlow model on a structured data set with 100 billion records stored in several CSV files. You need to improve the input/output execution performance. What should you do?
- A. Convert the CSV files into shards of TFRecords, and store the data in Cloud Storage
- B. Load the data into Cloud Bigtable, and read the data from Bigtable
- C. Convert the CSV files into shards of TFRecords, and store the data in the Hadoop Distributed File System (HDFS)
- D. Load the data into BigQuery and read the data from BigQuery.
正解：B
質問 # 49
A Machine Learning Specialist at a company sensitive to security is preparing a dataset for model training. The dataset is stored in Amazon S3 and contains Personally Identifiable Information (PII).
The dataset:
* Must be accessible from a VPC only.
* Must not traverse the public internet.
How can these requirements be satisfied?
- A. Create a VPC endpoint and apply a bucket access policy that restricts access to the given VPC endpoint and the VPC.
- B. Create a VPC endpoint and use security groups to restrict access to the given VPC endpoint and an Amazon EC2 instance
- C. Create a VPC endpoint and apply a bucket access policy that allows access from the given VPC endpoint and an Amazon EC2 instance.
- D. Create a VPC endpoint and use Network Access Control Lists (NACLs) to allow traffic between only the given VPC endpoint and an Amazon EC2 instance.
正解：A
質問 # 50
You have trained a model on a dataset that required computationally expensive preprocessing operations. You need to execute the same preprocessing at prediction time. You deployed the model on Al Platform for high-throughput online prediction. Which architecture should you use?
- A. Send incoming prediction requests to a Pub/Sub topic
* Set up a Cloud Function that is triggered when messages are published to the Pub/Sub topic.
* Implement your preprocessing logic in the Cloud Function
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue - B. Send incoming prediction requests to a Pub/Sub topic
* Transform the incoming data using a Dataflow job
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue - C. Validate the accuracy of the model that you trained on preprocessed data
* Create a new model that uses the raw data and is available in real time
* Deploy the new model onto Al Platform for online prediction - D. Stream incoming prediction request data into Cloud Spanner
* Create a view to abstract your preprocessing logic.
* Query the view every second for new records
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue.
正解：A
質問 # 51
You have trained a text classification model in TensorFlow using Al Platform. You want to use the trained model for batch predictions on text data stored in BigQuery while minimizing computational overhead. What should you do?
- A. Use Dataflow with the SavedModel to read the data from BigQuery
- B. Export the model to BigQuery ML.
- C. Submit a batch prediction job on Al Platform that points to the model location in Cloud Storage.
- D. Deploy and version the model on Al Platform.
正解：B
質問 # 52
You started working on a classification problem with time series data and achieved an area under the receiver operating characteristic curve (AUC ROC) value of 99% for training data after just a few experiments. You haven't explored using any sophisticated algorithms or spent any time on hyperparameter tuning. What should your next step be to identify and fix the problem?
- A. Address data leakage by removing features highly correlated with the target value.
- B. Address the model overfitting by using a less complex algorithm.
- C. Address the model overfitting by tuning the hyperparameters to reduce the AUC ROC value.
- D. Address data leakage by applying nested cross-validation during model training.
正解：D
質問 # 53
You are an ML engineer at a mobile gaming company. A data scientist on your team recently trained a TensorFlow model, and you are responsible for deploying this model into a mobile application. You discover that the inference latency of the current model doesn't meet production requirements. You need to reduce the inference time by 50%, and you are willing to accept a small decrease in model accuracy in order to reach the latency requirement. Without training a new model, which model optimization technique for reducing latency should you try first?
- A. Dimensionality reduction
- B. Dynamic range quantization
- C. Weight pruning
- D. Model distillation
正解：D
質問 # 54
You need to analyze user activity data from your company's mobile applications. Your team will use BigQuery for data analysis, transformation, and experimentation with ML algorithms. You need to ensure real-time ingestion of the user activity data into BigQuery. What should you do?
- A. Run an Apache Spark streaming job on Dataproc to ingest the data into BigQuery.
- B. Configure Pub/Sub to stream the data into BigQuery.
- C. Configure Pub/Sub and a Dataflow streaming job to ingest the data into BigQuery,
- D. Run a Dataflow streaming job to ingest the data into BigQuery.
正解：B
解説：
Pub/Sub is a messaging service that can be used to stream data into BigQuery in real-time. Configuring Pub/Sub to stream the user activity data into BigQuery would ensure real-time ingestion of the data. Source: Google Cloud
質問 # 55
An online reseller has a large, multi-column dataset with one column missing 30% of its data. A Machine Learning Specialist believes that certain columns in the dataset could be used to reconstruct the missing data.
Which reconstruction approach should the Specialist use to preserve the integrity of the dataset?
- A. Listwise deletion
- B. Last observation carried forward
- C. Mean substitution
- D. Multiple imputation
正解：D
解説：
Explanation/Reference: https://worldwidescience.org/topicpages/i/imputing+missing+values.html
質問 # 56
A company is observing low accuracy while training on the default built-in image classification algorithm in Amazon SageMaker. The Data Science team wants to use an Inception neural network architecture instead of a ResNet architecture.
Which of the following will accomplish this? (Choose two.)
- A. Use custom code in Amazon SageMaker with TensorFlow Estimator to load the model with an Inception network, and use this for model training.
- B. Download and apt-get installthe inception network code into an Amazon EC2 instance and use this instance as a Jupyter notebook in Amazon SageMaker.
- C. Create a support case with the SageMaker team to change the default image classification algorithm to Inception.
- D. Bundle a Docker container with TensorFlow Estimator loaded with an Inception network and use this for model training.
- E. Customize the built-in image classification algorithm to use Inception and use this for model training.
正解：A、E
質問 # 57
You are an ML engineer at a global car manufacturer. You need to build an ML model to predict car sales in different cities around the world. Which features or feature crosses should you use to train city-specific relationships between car type and number of sales?
- A. One feature obtained as an element-wise product between binned latitude, binned longitude, and one-hot encoded car type
- B. One feature obtained as an element-wise product between latitude, longitude, and car type
- C. Three individual features binned latitude, binned longitude, and one-hot encoded car type
- D. Two feature crosses as a element-wise product the first between binned latitude and one-hot encoded car type, and the second between binned longitude and one-hot encoded car type
正解：C
質問 # 58
A Data Scientist needs to create a serverless ingestion and analytics solution for high-velocity, real-time streaming data.
The ingestion process must buffer and convert incoming records from JSON to a query-optimized, columnar format without data loss. The output datastore must be highly available, and Analysts must be able to run SQL queries against the data and connect to existing business intelligence dashboards.
Which solution should the Data Scientist build to satisfy the requirements?
- A. Write each JSON record to a staging location in Amazon S3. Use the S3 Put event to trigger an AWS Lambda function that transforms the data into Apache Parquet or ORC format and writes the data to a processed data location in Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena, and connect to BI tools using the Athena Java Database Connectivity (JDBC) connector.
- B. Create a schema in the AWS Glue Data Catalog of the incoming data format. Use an Amazon Kinesis Data Firehose delivery stream to stream the data and transform the data to Apache Parquet or ORC format using the AWS Glue Data Catalog before delivering to Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena, and connect to BI tools using the Athena Java Database Connectivity (JDBC) connector.
- C. Use Amazon Kinesis Data Analytics to ingest the streaming data and perform real-time SQL queries to convert the records to Apache Parquet before delivering to Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena and connect to BI tools using the Athena Java Database Connectivity (JDBC) connector.
- D. Write each JSON record to a staging location in Amazon S3. Use the S3 Put event to trigger an AWS Lambda function that transforms the data into Apache Parquet or ORC format and inserts it into an Amazon RDS PostgreSQL database. Have the Analysts query and run dashboards from the RDS database.
正解：B
解説：
Explanation/Reference:
質問 # 59
You are developing ML models with Al Platform for image segmentation on CT scans. You frequently update your model architectures based on the newest available research papers, and have to rerun training on the same dataset to benchmark their performance. You want to minimize computation costs and manual intervention while having version control for your code. What should you do?
- A. Create an automated workflow in Cloud Composer that runs daily and looks for changes in code in Cloud Storage using a sensor.
- B. Use the gcloud command-line tool to submit training jobs on Al Platform when you update your code
- C. Use Cloud Build linked with Cloud Source Repositories to trigger retraining when new code is pushed to the repository
- D. Use Cloud Functions to identify changes to your code in Cloud Storage and trigger a retraining job
正解：B
質問 # 60
You are training a TensorFlow model on a structured data set with 100 billion records stored in several CSV files. You need to improve the input/output execution performance. What should you do?
- A. Convert the CSV files into shards of TFRecords, and store the data in Cloud Storage
- B. Convert the CSV files into shards of TFRecords, and store the data in the Hadoop Distributed File System (HDFS)
- C. Load the data into BigQuery and read the data from BigQuery.
- D. Load the data into Cloud Bigtable, and read the data from Bigtable
正解：A
質問 # 61
You are an ML engineer at an ecommerce company and have been tasked with building a model that predicts how much inventory the logistics team should order each month. Which approach should you take?
- A. Use a classification model to classify inventory levels as UNDER_STOCKED, OVER_STOCKED, and CORRECTLY_STOCKED. Give the report to the logistics team each month so they can fine-tune inventory levels.
- B. Use a time series forecasting model to predict each item's monthly sales. Give the results to the logistics team so they can base inventory on the amount predicted by the model.
- C. Use a regression model to predict how much additional inventory should be purchased each month. Give the results to the logistics team at the beginning of the month so they can increase inventory by the amount predicted by the model.
- D. Use a clustering algorithm to group popular items together. Give the list to the logistics team so they can increase inventory of the popular items.
正解：C
質問 # 62
You have trained a model on a dataset that required computationally expensive preprocessing operations. You need to execute the same preprocessing at prediction time. You deployed the model on Al Platform for high-throughput online prediction. Which architecture should you use?
- A. * Validate the accuracy of the model that you trained on preprocessed data
* Create a new model that uses the raw data and is available in real time
* Deploy the new model onto Al Platform for online prediction - B. * Send incoming prediction requests to a Pub/Sub topic
* Transform the incoming data using a Dataflow job
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue - C. * Send incoming prediction requests to a Pub/Sub topic
* Set up a Cloud Function that is triggered when messages are published to the Pub/Sub topic.
* Implement your preprocessing logic in the Cloud Function
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue - D. * Stream incoming prediction request data into Cloud Spanner
* Create a view to abstract your preprocessing logic.
* Query the view every second for new records
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue.
正解：B
解説：
https://cloud.google.com/architecture/data-preprocessing-for-ml-with-tf-transform-pt1#where_to_do_preprocessing
質問 # 63
You are working on a Neural Network-based project. The dataset provided to you has columns with different ranges. While preparing the data for model training, you discover that gradient optimization is having difficulty moving weights to a good solution. What should you do?
- A. Improve the data cleaning step by removing features with missing values.
- B. Use the representation transformation (normalization) technique.
- C. Change the partitioning step to reduce the dimension of the test set and have a larger training set.
- D. Use feature construction to combine the strongest features.
正解：A
質問 # 64
You work as an ML engineer at a social media company, and you are developing a visual filter for users' profile photos. This requires you to train an ML model to detect bounding boxes around human faces. You want to use this filter in your company's iOS-based mobile phone application. You want to minimize code development and want the model to be optimized for inference on mobile phones. What should you do?
- A. Train a model using AutoML Vision and use the "export for TensorFlow.js" option.
- B. Train a model using AutoML Vision and use the "export for Coral" option.
- C. Train a custom TensorFlow model and convert it to TensorFlow Lite (TFLite).
- D. Train a model using AutoML Vision and use the "export for Core ML" option.
正解：D
質問 # 65
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2023年最新の実際にある検証済みのProfessional-Machine-Learning-Engineer問題集：https://jp.fast2test.com/Professional-Machine-Learning-Engineer-premium-file.html
合格させるProfessional-Machine-Learning-Engineer試験で更新された150問題あります：https://drive.google.com/open?id=1MD-3imVkYDmM0FOM9RjhWRfP-JuLzlp0